The public sector pay premium, compensating differentials and unions: propensity score matching evidence from Australia, Canada, Great Britain and the United States
Why this work is in the frame
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Bibliographic record
Abstract
Propensity score matching is used to estimate the size of the public sector pay premium in four countries. Three sets of matching covariates are used; worker characteristics only, then including job attributes and finally adding union membership. When worker characteristics and job attributes are controlled for, the public sector pay premium ranges from 30% in Canada to 19-20% in Australia and Great Britain and only 6% in the United States. Differences in job attributes between private sector and public sector workers make almost no difference to the estimated pay premium. But once differences in union membership across sectors are controlled for, the estimated public sector pay premium is reduced in all countries and disappears in Canada. This finding favors the hypothesis that the pay premium partially reflects rents accruing to public sector workers, obtained most probably with assistance from the actions of their labor unions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it